Dissociable and Common Mechanisms Underlying Value and Uncertainty Learning
Poster No:
748
Submission Type:
Abstract Submission
Authors:
Alejandra Figueroa-Vargas1, Paulo Figueroa-Taiba2, Marcela Díaz Díaz3, Mariana Ayala-Ochoa4, Patricio Carvajal-Paredes5, Francisco Zamorano6, Victor Marquez2, Patricia Soto-Icaza5, Rafael Polania7, Pablo Billeke5
Institutions:
1Pontificia Universidad Catolica de Chile, Santiago, Santiago, 2Universidad del Desarrollo, Santiago, Santiago, 3Pontificia Universidad Católica de Chile, Santiago, Santiago, 4Universidad del Desarrollo, Santiago, s, 5Universidad del Desarrollo, Santiago, WI, 6Universidad San Sebastián, Santiago, Chile, 7ETH, Zurich, Switzerland
First Author:
Co-Author(s):
Introduction:
Understanding how humans adapt to uncertain environments is essential for uncovering the cognitive mechanisms that underlie decision-making and their potential disruption in anxiety disorders. Uncertainty is critical in shaping attention, learning, and behavior (Valdebenito-Oyarzo et al., 2024). However, the exact mechanisms by which individuals estimate and adapt to uncertainty, as well as whether uncertainty is perceived as a distinct environmental property or an intrinsic feature of specific stimuli, remain unclear. To explore these questions, we developed the Learning Under Uncertainty (LUU) task, specifically designed to disentangle value learning from uncertainty learning, enabling their independent evaluation.
Methods:
In the LUU task, participants predicted the landing position of a target butterfly following noisy trajectories, while distractors occasionally appeared to introduce additional complexity. This dynamic setup required participants to learn target-related patterns while adapting to environmental uncertainty. Notably, the distractors shared some features with the target, which could be interpreted as environmental uncertainty, but they provided no direct information about the target's value (Figure 1). 67 healthy participants completed 151 experimental sessions, encompassing behavioral, fMRI, and EEG recordings. Behavioral data were analyzed using Bayesian cognitive computational models, including variations of the Rescorla-Wagner algorithm, which allowed for trial-by-trial adjustments in value learning and independent mechanisms for learning about uncertainty and the effects of distractors. Structural and functional imaging data were acquired using a Siemens 3T Skyra scanner, while a 64-electrode EEG BrainAmp system was employed to record brain electrical activity.
·Figure1
Results:
Models that independently assessed value and uncertainty learning significantly outperformed simpler alternatives, such as null and standard Rescorla-Wagner models, based on model comparison metrics. The best-fitting model revealed that participants dynamically adjusted their learning rates by integrating environmental uncertainty. Importantly, distractors significantly reduced the magnitude of value (μ) updates without altering uncertainty (ξ) updates, highlighting a dissociable behavioral effect between these two processes. Moreover, the impact of distractors was more pronounced under conditions of high uncertainty. It correlated with trait anxiety symptoms, underscoring the critical role of perceived environmental variability in shaping adaptive behavior.
Univariate and multivariate neuroimaging analyses revealed dissociable and shared neural correlates of value and uncertainty learning. Value updates were associated with activity in medial brain regions, including the orbitofrontal cortex and ventromedial prefrontal cortex, whereas uncertainty processing engaged parietal and midcingulate regions. Notably, the parietal region exhibited shared encoding of value updates and uncertainty, suggesting a convergence of these processes. EEG analyses further demonstrated that centro-frontal and parietal theta oscillations were modulated by trial-by-trial uncertainty during decision-making (Figure 2).
Univariate and multivariate neuroimaging analyses revealed dissociable and shared neural correlates of value and uncertainty learning. Value updates were associated with activity in medial brain regions, including the orbitofrontal cortex and ventromedial prefrontal cortex, whereas uncertainty processing engaged parietal and midcingulate regions. Notably, the parietal region exhibited shared encoding of value updates and uncertainty, suggesting a convergence of these processes. EEG analyses further demonstrated that centro-frontal and parietal theta oscillations were modulated by trial-by-trial uncertainty during decision-making (Figure 2).
·Figure 2
Conclusions:
Integrating computational models with neural data has provided novel insights into the distinct and shared mechanisms underlying value and uncertainty learning. First, uncertainty was found to dynamically scale prediction error signals, influencing the magnitude of learning rates and susceptibility to distractors. Second, updates to uncertainty occurred independently of value updates, suggesting that humans perceive uncertainty as a global environmental feature rather than a property tied to specific stimuli. These findings have broad implications for understanding adaptive behavior and its dysregulation in clinical populations.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Learning and Memory:
Learning and Memory Other 2
Keywords:
Anxiety
Electroencephaolography (EEG)
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Martínez-Molina, M. P. et al. Lateral prefrontal theta oscillations causally drive a computational mechanism underlying conflict expectation and adaptation. Nat. Commun. 15, 9858 (2024).